Dementia diseases are increasing rapidly, according to the World Health Organization
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …

Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …

In recent years neural networks have been used to achieve all 17 SDGs. This paper is
directly related to SDG 9. In particular, the application of neural networks in statistics …

Background Physicians follow-up a symptom-based approach in the diagnosis of psychiatric
diseases. According to this approach, a process based on internationally valid diagnostic …

Internet addiction (IA), as a new and often unrecognized psychosocial disorder, endangers
people's health and their lives. However, the common biometric analysis based on the …

Electroencephalography (EEG) signals are known to be nonstationary and often
multicomponential signals containing information about the condition of the brain. Since the …

Arsenic contamination in the groundwater occurs in various parts of the world due to
anthropogenic and natural sources, adversely affecting human health and ecosystems. The …

Background Generalized regression neural network (GRNN) and logistic regression (LR)
are extensively used in the medical field; however, the better model for predicting stroke …

Previous studies on EEG activity in prescription opioid use disorder (OUD) have reported
neuronal dysfunction related to heroin use, most consistently reflected by increases in β …